Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond

arXiv:2404.17454v1 Announce Type: new
Abstract: Fined-grained anomalous cell detection from affected tissues is critical for clinical diagnosis and pathological research. Single-cell sequencing data provide unprecedented opportunities for this task. However, current anomaly detection methods struggle to handle domain shifts prevalent in multi-sample and multi-domain single-cell sequencing data, leading to suboptimal performance. Moreover, these methods fall short of distinguishing anomalous cells into pathologically distinct subtypes. In response, we propose ACSleuth, a novel, reconstruction deviation-guided generative framework that integrates the detection, domain …

anomaly anomaly detection arxiv beyond cs.ai cs.lg data detection domain fine-grained q-bio.qm sequencing type